import cv2
import os
import os.path
import sys
import torch
import matplotlib.pyplot as plt
import numpy as np
from network import Model
import torch.nn as nn
import torch.optim as optim
from tqdm.notebook import tqdm
from torch.utils.data import Dataset, DataLoader
from network import Model

width = 25
height = 25
channel = 1
video_path = '../InputVideo/Train_Video'
label_path = '../InputVideo/Train_Label'

File Loader

files = {'all_video': [f for f in sorted(os.listdir(video_path)) if f.endswith('.avi')],
         'all_label': [f for f in sorted(os.listdir(label_path)) if f.endswith('.txt')],
         'train_video': [],
         'train_label': [],
         'test_video': [],
         'test_label': []}

for index, (video, label) in enumerate(zip(files['all_video'], files['all_label'])):
    if index % 4 == 0:
        files['test_video'].append(video)
        files['test_label'].append(label)
    else:
        files['train_video'].append(video)
        files['train_label'].append(label)

for name, fileList in files.items():
    print(name + '\r')
    print(fileList)

all_video
['STMD_0.avi', 'STMD_1.avi', 'STMD_2.avi', 'STMD_3.avi', 'STMD_4.avi', 'STMD_5.avi', 'STMD_6.avi', 'STMD_7.avi', 'STMD_8.avi', 'STMD_9.avi']
all_label
['STMD_0.txt', 'STMD_1.txt', 'STMD_2.txt', 'STMD_3.txt', 'STMD_4.txt', 'STMD_5.txt', 'STMD_6.txt', 'STMD_7.txt', 'STMD_8.txt', 'STMD_9.txt']
train_video
['STMD_1.avi', 'STMD_2.avi', 'STMD_3.avi', 'STMD_5.avi', 'STMD_6.avi', 'STMD_7.avi', 'STMD_9.avi']
train_label
['STMD_1.txt', 'STMD_2.txt', 'STMD_3.txt', 'STMD_5.txt', 'STMD_6.txt', 'STMD_7.txt', 'STMD_9.txt']
test_video
['STMD_0.avi', 'STMD_4.avi', 'STMD_8.avi']
test_label
['STMD_0.txt', 'STMD_4.txt', 'STMD_8.txt']

Video and label loader and preprocessing

variable name: videos
type: dictionary, key: 视频文件名称
                  value: tensor张量(shape = [9000, 1, 25, 25], [num of frames, channel, width, height])
variable name: spike_times
type: dictionary, key: label文件名称
                  value: tensor张量（shape = [9000], [num of frames])
                  description: one-hot vector with spiking time of 1, 0 otherwise

num_frames = 0

## Load Videos with frames
videos = {}
for filename in files['all_video']:
    videoLoader = cv2.VideoCapture(video_path + "/" + filename)
    n = 0
    frame_tensors = []
    while True:
        ret, frame = videoLoader.read()
        if ret is False:
            break
        frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)         # 1. 将BGR图像转换为灰度图
        frame_gray = frame_gray.astype(np.float32) / 255.0           # 2. 将数据类型转换为float32，并缩放到0-1范围
        frame_resized = cv2.resize(frame_gray, (width, height))      # 3. 重新调整图像尺寸（如果需要保持原尺寸，这一步可以省略）       
        frame_tensor = torch.tensor(frame_resized).unsqueeze(0)
        frame_tensors.append(frame_tensor)
    num_frames = len(frame_tensors)
    videoLoader.release()
    videos[filename] = torch.stack(frame_tensors) ## shape: [1, 25, 25] [channel, width, height]

print(videos['STMD_0.avi'].shape)

## Load Labels
spike_times = {}
for labelName in files['all_label']:
    spikes = [0] * num_frames
    with open(label_path + "/" + labelName, 'r') as file:
        for line in file:
            try:
                spike_index = int(line.strip())
                spikes[spike_index] = 1
            except ValueError:
                print(f"Skipping invalid number: {line.strip()}")
    spike_tensor = torch.tensor([element for element in spikes])
    spike_times[labelName] = spike_tensor

torch.Size([9000, 1, 25, 25])

plt.figure(figsize = (16,5))
for index, (name, one_hot) in enumerate(spike_times.items()):
    one_array = one_hot.numpy()
    plt.subplot(2,5, index + 1)
    plt.tight_layout()
    plt.plot(one_array)
    plt.xlabel('Index of frame')
    plt.ylabel('Hot')
    plt.title('Name')

plt.show()

def get_window_size(spike_times):
    lengths = []
    for one_hot in spike_times.values():
        count = 0
        for item in one_hot:
            if item == 1:
                count += 1
            else:
                if count > 0:
                    lengths.append(count)
                    count = 0 
    
        if count > 0:
            lengths.append(count)  # 处理数组末尾的连续1
            
    if lengths:
        avg_length =  sum(lengths) / len(lengths)  # 计算平均长度
    else:
        avg_length = 0
    return (lengths, (round(avg_length) + 1)*2)

lengths, window_size = get_window_size(spike_times)
print(window_size)
plt.figure
plt.hist(lengths, bins=21)
plt.xlabel('length of spike time')
plt.ylabel('times')
plt.title('The distribution of spike time length')
plt.show()
print(sorted(lengths))

10

[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 13, 13, 13, 13, 13, 15, 18, 19, 21, 23, 24, 31, 46]

import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np

class VideoFrameDataset(Dataset):
    def __init__(self, video_frames_dict, labels_dict, window_size):
        self.video_frames_dict = video_frames_dict
        self.labels_dict = labels_dict
        self.window_size = window_size
        self.samples = self._generate_samples()

    def _generate_samples(self):
        samples = []
        for filename, frames in self.video_frames_dict.items():
            num_frames = len(frames)
            for start in range(0, (num_frames - self.window_size + 1), 2):
                end = start + self.window_size
                samples.append((filename, start, end))
        return samples
        
    def __len__(self):
        return len(self.samples)

    def __getitem__(self, idx):
        filename, start, end = self.samples[idx]
        frames = self.video_frames_dict[filename][start:end, :, :]  # [window_size, 1, 25, 25]
        labels = self.labels_dict[os.path.splitext(filename)[0] + '.txt'][start:end]  # [sequence_length, num_classes]
        return frames, labels

def show_image(tensor_list, title):
    
    # 将选定的帧转换为numpy数组
    frame_np = tensor_list.squeeze().detach().numpy()  # 直接使用squeeze()和numpy()，省略permute()
    
    # 将数据类型从float转换回uint8（如果之前进行了归一化）
    frame_np = (frame_np * 255).astype(np.uint8)
    
    plt.imshow(frame_np, cmap='gray')  # 对于灰度图，确保使用正确的颜色映射
    plt.axis('off')  # 不显示坐标轴
    plt.title(title)
    plt.show()

train_video={}
train_label={}
test_video={}
test_label={}
for name in files['train_video']:
    label_name = os.path.splitext(name)[0] + '.txt'
    train_video[name] = videos[name]
    train_label[label_name] = spike_times[label_name]

for name in files['test_video']:
    label_name = os.path.splitext(name)[0] + '.txt'
    test_video[name] = videos[name]
    test_label[label_name] = spike_times[label_name]

print(train_video.keys())
print(train_label.keys())
print(test_video.keys())
print(test_label.keys())

dict_keys(['STMD_1.avi', 'STMD_2.avi', 'STMD_3.avi', 'STMD_5.avi', 'STMD_6.avi', 'STMD_7.avi', 'STMD_9.avi'])
dict_keys(['STMD_1.txt', 'STMD_2.txt', 'STMD_3.txt', 'STMD_5.txt', 'STMD_6.txt', 'STMD_7.txt', 'STMD_9.txt'])
dict_keys(['STMD_0.avi', 'STMD_4.avi', 'STMD_8.avi'])
dict_keys(['STMD_0.txt', 'STMD_4.txt', 'STMD_8.txt'])

Data Load process

## Hyperparameter Setting
window_size = 100
batch_size = 10

# Assuming `video_frames_dict` and `labels_dict` are your dictionaries

train_set = VideoFrameDataset(train_video, train_label, window_size)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)

test_set = VideoFrameDataset(test_video, test_label, window_size)
test_loader = DataLoader(test_set, batch_size=batch_size)

print(len(train_loader))
# In your training loop
data_iter = iter(train_loader)
frames, labels = next(data_iter)
print(frames.shape)
first_item_batch = frames[0]
# plt.figure()
# for index, frame in enumerate(first_item_batch):
#     show_image(frame, f'the {index}th image')
# print(labels[0])

3116
torch.Size([10, 100, 1, 25, 25])

前期数据加载完成：

1. 数据以dataloader的形式存储,分别以train_loader, test_loader 存储训练集和测试集
2. 训练集和测试集都包含: \
   1. frames.shape = [10,5,1,25,25]: [batch size, frame num, channel, width, height] \
   2. label.shape = [10,5]: [batch size, frame 1-hot]

LSTM 模型构建与autoencoder应用

目标：1. 对现有数据集进行降维处理以及特征提取

 2. 将连续帧输入模型
 3. 输出结果为每组5个标签。

n_samples 样本量为10 3149 一共31490个样本 n_timesteps 时间步，每个样本含有五个时间步 n_features 特征数：每张图像含有2525个特征

1. 对图像进行降维处理: LSTM encoder

'''
from LSTM import LinearAutoencoder2

linear2 = LinearAutoencoder2()
criterion = nn.MSELoss()
learning_rate = 0.0001
optimizer = optim.Adam(linear2.parameters(), lr=learning_rate)
EPOCHS = 20
loss_value = []
linear2.train()
for epoch in range(1, EPOCHS + 1):
    loss = 0
    processBar = tqdm(train_loader, unit='step')
    for step, (trainImgs, labels) in enumerate(processBar):
        optimizer.zero_grad()
        # 前向传播
        outputs = linear2(trainImgs)
        # 计算损失
        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构
        # 反向传播
        loss.backward()
        # 参数更新
        optimizer.step()
        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")
        loss_value.append(loss.item())

plt.figure()
plt.plot(loss_value)
plt.title("Training Loss Curve")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.show()
'''

'\nfrom LSTM import LinearAutoencoder2\n\nlinear2 = LinearAutoencoder2()\ncriterion = nn.MSELoss()\nlearning_rate = 0.0001\noptimizer = optim.Adam(linear2.parameters(), lr=learning_rate)\nEPOCHS = 20\nloss_value = []\nlinear2.train()\nfor epoch in range(1, EPOCHS + 1):\n    loss = 0\n    processBar = tqdm(train_loader, unit=\'step\')\n    for step, (trainImgs, labels) in enumerate(processBar):\n        optimizer.zero_grad()\n        # 前向传播\n        outputs = linear2(trainImgs)\n        # 计算损失\n        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构\n        # 反向传播\n        loss.backward()\n        # 参数更新\n        optimizer.step()\n        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")\n        loss_value.append(loss.item())\n\nplt.figure()\nplt.plot(loss_value)\nplt.title("Training Loss Curve")\nplt.xlabel("Epoch")\nplt.ylabel("Loss")\nplt.show()\n'

'''
show_image(outputs[0][0], title = 'output image')
show_image(trainImgs[0][0], title = 'original image')

linear2.eval()  # 将模型设置为评估模式

test_loss = 0
with torch.no_grad():  # 在评估过程中不计算梯度
    for testImgs, _ in test_loader:  # 假设test_loader是您的测试数据加载器
        # 前向传播
        reconstructed_imgs = linear2(testImgs)
        # 计算损失
        test_loss += criterion(reconstructed_imgs, testImgs).item()

# 计算平均损失
avg_test_loss = test_loss / len(test_loader)
print(f'Average Test Loss: {avg_test_loss:.4f}')

show_image(reconstructed_imgs[0][0], title = 'reconstructed image')
show_image(testImgs[0][0], title = 'original tested image')
'''

"\nshow_image(outputs[0][0], title = 'output image')\nshow_image(trainImgs[0][0], title = 'original image')\n\nlinear2.eval()  # 将模型设置为评估模式\n\ntest_loss = 0\nwith torch.no_grad():  # 在评估过程中不计算梯度\n    for testImgs, _ in test_loader:  # 假设test_loader是您的测试数据加载器\n        # 前向传播\n        reconstructed_imgs = linear2(testImgs)\n        # 计算损失\n        test_loss += criterion(reconstructed_imgs, testImgs).item()\n\n# 计算平均损失\navg_test_loss = test_loss / len(test_loader)\nprint(f'Average Test Loss: {avg_test_loss:.4f}')\n\nshow_image(reconstructed_imgs[0][0], title = 'reconstructed image')\nshow_image(testImgs[0][0], title = 'original tested image')\n"

'''
from LSTM import LSTMEncoder, LSTMDecoder, LSTMAutoencoder

LSTMautoencoder = LSTMAutoencoder()
criterion = nn.MSELoss()
learning_rate = 0.0001
optimizer = optim.Adam(LSTMautoencoder.parameters(), lr=learning_rate)
EPOCHS = 10
loss_value = []
LSTMautoencoder.train()
for epoch in range(1, EPOCHS + 1):
    loss = 0
    processBar = tqdm(train_loader, unit='step')
    for step, (trainImgs, labels) in enumerate(processBar):
        optimizer.zero_grad()
        # 前向传播
        outputs = LSTMautoencoder(trainImgs)
        # 计算损失
        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构
        # 反向传播
        loss.backward()
        # 参数更新
        optimizer.step()
        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")
        loss_value.append(loss.item())


plt.figure()
plt.plot(loss_value)
plt.title("Training Loss Curve")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.show()


show_image(outputs[0][0], title = 'output image')
show_image(trainImgs[0][0], title = 'original image')

LSTMautoencoder.eval()  # 将模型设置为评估模式

test_loss = 0
with torch.no_grad():  # 在评估过程中不计算梯度
    for testImgs, _ in test_loader:  # 假设test_loader是您的测试数据加载器
        # 前向传播
        reconstructed_imgs = LSTMautoencoder(testImgs)
        # 计算损失
        test_loss += criterion(reconstructed_imgs, testImgs).item()

# 计算平均损失
avg_test_loss = test_loss / len(test_loader)
print(f'Average Test Loss: {avg_test_loss:.4f}')

show_image(reconstructed_imgs[0][0], title = 'reconstructed image')
show_image(testImgs[0][0], title = 'original tested image')
'''

'\nfrom LSTM import LSTMEncoder, LSTMDecoder, LSTMAutoencoder\n\nLSTMautoencoder = LSTMAutoencoder()\ncriterion = nn.MSELoss()\nlearning_rate = 0.0001\noptimizer = optim.Adam(LSTMautoencoder.parameters(), lr=learning_rate)\nEPOCHS = 10\nloss_value = []\nLSTMautoencoder.train()\nfor epoch in range(1, EPOCHS + 1):\n    loss = 0\n    processBar = tqdm(train_loader, unit=\'step\')\n    for step, (trainImgs, labels) in enumerate(processBar):\n        optimizer.zero_grad()\n        # 前向传播\n        outputs = LSTMautoencoder(trainImgs)\n        # 计算损失\n        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构\n        # 反向传播\n        loss.backward()\n        # 参数更新\n        optimizer.step()\n        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")\n        loss_value.append(loss.item())\n\n\nplt.figure()\nplt.plot(loss_value)\nplt.title("Training Loss Curve")\nplt.xlabel("Epoch")\nplt.ylabel("Loss")\nplt.show()\n\n\nshow_image(outputs[0][0], title = \'output image\')\nshow_image(trainImgs[0][0], title = \'original image\')\n\nLSTMautoencoder.eval()  # 将模型设置为评估模式\n\ntest_loss = 0\nwith torch.no_grad():  # 在评估过程中不计算梯度\n    for testImgs, _ in test_loader:  # 假设test_loader是您的测试数据加载器\n        # 前向传播\n        reconstructed_imgs = LSTMautoencoder(testImgs)\n        # 计算损失\n        test_loss += criterion(reconstructed_imgs, testImgs).item()\n\n# 计算平均损失\navg_test_loss = test_loss / len(test_loader)\nprint(f\'Average Test Loss: {avg_test_loss:.4f}\')\n\nshow_image(reconstructed_imgs[0][0], title = \'reconstructed image\')\nshow_image(testImgs[0][0], title = \'original tested image\')\n'

'''
from LSTM import LSTMCovAutoencoder

autoencoder = LSTMCovAutoencoder()
criterion = nn.MSELoss()
learning_rate = 0.0001
optimizer = optim.Adam(autoencoder.parameters(), lr=learning_rate)
EPOCHS = 10
loss_value = []
for epoch in range(1, EPOCHS + 1):
    loss = 0
    processBar = tqdm(train_loader, unit='step')
    for step, (trainImgs, labels) in enumerate(processBar):
        optimizer.zero_grad()
        # 前向传播
        outputs = autoencoder(trainImgs)
        # 计算损失
        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构
        # 反向传播
        loss.backward()
        # 参数更新
        optimizer.step()
        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")
        loss_value.append(loss.item())

show_image(outputs[0][0])
'''

'\nfrom LSTM import LSTMCovAutoencoder\n\nautoencoder = LSTMCovAutoencoder()\ncriterion = nn.MSELoss()\nlearning_rate = 0.0001\noptimizer = optim.Adam(autoencoder.parameters(), lr=learning_rate)\nEPOCHS = 10\nloss_value = []\nfor epoch in range(1, EPOCHS + 1):\n    loss = 0\n    processBar = tqdm(train_loader, unit=\'step\')\n    for step, (trainImgs, labels) in enumerate(processBar):\n        optimizer.zero_grad()\n        # 前向传播\n        outputs = autoencoder(trainImgs)\n        # 计算损失\n        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构\n        # 反向传播\n        loss.backward()\n        # 参数更新\n        optimizer.step()\n        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")\n        loss_value.append(loss.item())\n\nshow_image(outputs[0][0])\n'

'''
from LSTM import LinearAutoencoder

autoencoder = LinearAutoencoder()
criterion = nn.MSELoss()
learning_rate = 0.0001
optimizer = optim.Adam(autoencoder.parameters(), lr=learning_rate)
EPOCHS = 10
loss_value = []
autoencoder.train()
for epoch in range(1, EPOCHS + 1):
    loss = 0
    processBar = tqdm(train_loader, unit='step')
    for step, (trainImgs, labels) in enumerate(processBar):
        optimizer.zero_grad()
        # 前向传播
        outputs = autoencoder(trainImgs)
        # 计算损失
        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构
        # 反向传播
        loss.backward()
        # 参数更新
        optimizer.step()
        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")
    loss_value.append(loss.item())

plt.figure()
plt.plot(loss_value)
plt.xlabel('num of epoch')
plt.ylabel('loss value')
plt.show()

autoencoder.eval()  # 将模型设置为评估模式

test_loss = 0
with torch.no_grad():  # 在评估过程中不计算梯度
    for testImgs, _ in test_loader:  # 假设test_loader是您的测试数据加载器
        # 前向传播
        reconstructed_imgs = autoencoder(testImgs)
        # 计算损失
        test_loss += criterion(reconstructed_imgs, testImgs).item()

# 计算平均损失
avg_test_loss = test_loss / len(test_loader)
print(f'Average Test Loss: {avg_test_loss:.4f}')

show_image(reconstructed_imgs[0][0], 'reconstructed image')
show_image(testImgs[0][0], 'original tested image')
'''

'\nfrom LSTM import LinearAutoencoder\n\nautoencoder = LinearAutoencoder()\ncriterion = nn.MSELoss()\nlearning_rate = 0.0001\noptimizer = optim.Adam(autoencoder.parameters(), lr=learning_rate)\nEPOCHS = 10\nloss_value = []\nautoencoder.train()\nfor epoch in range(1, EPOCHS + 1):\n    loss = 0\n    processBar = tqdm(train_loader, unit=\'step\')\n    for step, (trainImgs, labels) in enumerate(processBar):\n        optimizer.zero_grad()\n        # 前向传播\n        outputs = autoencoder(trainImgs)\n        # 计算损失\n        loss = criterion(outputs, trainImgs)  # 使用输入作为目标进行重构\n        # 反向传播\n        loss.backward()\n        # 参数更新\n        optimizer.step()\n        processBar.set_description(f"[{epoch}/{EPOCHS}] Loss: {loss.item():.10f}")\n    loss_value.append(loss.item())\n\nplt.figure()\nplt.plot(loss_value)\nplt.xlabel(\'num of epoch\')\nplt.ylabel(\'loss value\')\nplt.show()\n\nautoencoder.eval()  # 将模型设置为评估模式\n\ntest_loss = 0\nwith torch.no_grad():  # 在评估过程中不计算梯度\n    for testImgs, _ in test_loader:  # 假设test_loader是您的测试数据加载器\n        # 前向传播\n        reconstructed_imgs = autoencoder(testImgs)\n        # 计算损失\n        test_loss += criterion(reconstructed_imgs, testImgs).item()\n\n# 计算平均损失\navg_test_loss = test_loss / len(test_loader)\nprint(f\'Average Test Loss: {avg_test_loss:.4f}\')\n\nshow_image(reconstructed_imgs[0][0], \'reconstructed image\')\nshow_image(testImgs[0][0], \'original tested image\')\n'

def precision_cal(encoder, predictor, test_loader, save=False, calculate=True):
    encoder.eval()
    predictor.eval()  # 将模型设置为评估模式
    criterion = nn.BCELoss()
    avg_test_loss = 0
    avg_test_precision = 0
    sample_total = 0

    total_output = []
    threshold = 1.0e-01
    with torch.no_grad():  # 在评估过程中不计算梯度
        avg_correct = 0
        test_loss = 0
        avg_accuracy = 0
        TP = 0
        TN = 0
        FP = 0
        FN = 0
        
        for testImgs, testLabel in test_loader:  # 假设test_loader是您的测试数据加载器
            # 前向传播
            encoded,_ = encoder(testImgs)
            # 前向传播
            outputs = predictor(encoded)
            test_loss += criterion(outputs, testLabel.float()).item()
            batch_size, seq_len = outputs.size()
            if save: 
                result = torch.zeros([batch_size, seq_len], dtype=torch.int)
                for b_index, res_batch in enumerate(outputs):
                    for i_index, res_it in enumerate(res_batch):
                        if res_it >= 1.0e-01:
                            result[b_index][i_index] = 1
                total_output.append(result)
            
            if calculate: 
                for res_batch, label_batch in zip(outputs, testLabel): ## 遍历一个batch的sequence length组数据
                    for res_it, label_it in zip(res_batch, label_batch): ## 从每一组数据中取一帧的label
                        sample_total += 1
                        if res_it >= threshold and label_it == 1:
                            TP += 1
                        elif res_it <= threshold and label_it == 0:
                            TN += 1
                        elif res_it >= threshold and label_it == 0:
                            FP += 1
                        elif res_it <= threshold and label_it == 1:
                            FN += 1

    avg_accuracy = ((TP + TN) / sample_total)
    precision = TP/(TP + FP)        
    recall = TP / (TP+FN)
    # 计算平均损失
    avg_test_loss = test_loss / len(test_loader)

    return (avg_accuracy, precision, recall), avg_test_loss, total_output

from LSTM import Predictor, LSTMEncoder
encoder = LSTMEncoder()
predictor = Predictor()
criterion = nn.BCELoss()
learning_rate = 0.0001
optimizer = optim.Adam(predictor.parameters(), lr=learning_rate)
EPOCHS = 10
loss_value = []
predictor.train()
for epoch in range(1, EPOCHS + 1):
    loss = 0
    processBar = tqdm(train_loader, unit='step')
    for step, (trainImgs, labels) in enumerate(processBar):
        optimizer.zero_grad()
        encoded,_ = encoder(trainImgs)
        # 前向传播
        outputs = predictor(encoded)
        # 初始化BCE损失函数
        criterion = nn.BCELoss()
        # 计算损失
        loss = criterion(outputs, labels.float())
        # 反向传播
        loss.backward()
        # 参数更新
        optimizer.step()
        processBar.set_description(f"[{epoch}/{EPOCHS}] train Loss: {loss.item():.10f}")
    score, test_loss,_ = precision_cal(encoder, predictor, test_loader, calculate = True)
    print(f"Epoch {epoch}: scores for test = {score}, Avarage test loss = {test_loss}")
    loss_value.append(loss.item())

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 1: scores for test = (0.9452212985845877, 0.23576963069801765, 0.33764050341051016), Avarage test loss = 0.11411400178299581

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 2: scores for test = (0.9524825881824309, 0.3767208445418993, 0.800509174752618), Avarage test loss = 0.06937310574656425

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 3: scores for test = (0.9621058938066352, 0.44358148082878074, 0.8463589201652416), Avarage test loss = 0.056572060386195756

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 4: scores for test = (0.972198007938291, 0.5339227715626128, 0.8528196752810068), Avarage test loss = 0.050992416272688905

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 5: scores for test = (0.9698532165056541, 0.509450747984616, 0.893985973676626), Avarage test loss = 0.04410523114112327

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 6: scores for test = (0.9696667415562046, 0.5074899428329451, 0.9210779133442214), Avarage test loss = 0.04216741081481433

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 7: scores for test = (0.978782296113233, 0.6067462609923615, 0.9081083677586704), Avarage test loss = 0.0373716785573101

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 8: scores for test = (0.9771145061034974, 0.5852772260458527, 0.9129599385147469), Avarage test loss = 0.036047506838601515

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 9: scores for test = (0.9789620310042687, 0.6061841445057702, 0.9285233932174081), Avarage test loss = 0.03483774398415324

  0%|          | 0/3116 [00:00<?, ?step/s]

Epoch 10: scores for test = (0.9822032502059462, 0.6514319606846297, 0.9232635219521568), Avarage test loss = 0.03278866945234375

score, _, result = precision_cal(encoder, predictor, test_loader, calculate = True, save = True)
print(score)
for step, (_, labels) in enumerate(test_loader):
    print('result:','\n', result[step])
    print('label:', '\n', labels)

(0.9822032502059462, 0.6514319606846297, 0.9232635219521568)
result: 
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
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         0, 0, 0, 0],
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0]], dtype=torch.int32)
label: 
 tensor([[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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         0, 0, 0, 0],
        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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         0, 0, 0, 0],
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0]])
result: 
 tensor([[0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
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result: 
 tensor([[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
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result: 
 tensor([[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
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result: 
 tensor([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 

IOPub data rate exceeded.
The Jupyter server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--ServerApp.iopub_data_rate_limit`.

Current values:
ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)
ServerApp.rate_limit_window=3.0 (secs)

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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
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result: 
 tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
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label: 
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result: 
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label: 
 tensor([[0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
 tensor([[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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result: 
 tensor([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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label: 
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result: 
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         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
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         0, 0, 0, 0],
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         1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,
         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0]], dtype=torch.int32)
label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0]])
result: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0]], dtype=torch.int32)
label: 
 tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0]])

batch_size, seq_len = outputs.size()
result = torch.zeros([batch_size, seq_len], dtype=torch.int)
for b_index, res_batch in enumerate(outputs):
    for i_index, res_it in enumerate(res_batch):
        if res_it >= 1.0e-01:
            result[b_index][i_index] = 1
print(result, testLabel)